Principal Component Coordinates Research Articles

Stochastic competitive release and adaptive chemotherapy.

We develop a finite-cell model of tumor natural selection dynamics to investigate the stochastic fluctuations associated with multiple rounds of adaptive chemotherapy. The adaptive cycles are designed to avoid chemoresistance in the tumor by managing the ecological mechanism of competitive release of a resistant subpopulation. Our model is based on a three-component evolutionary game played among healthy (H), sensitive (S), and resistant (R) populations of N cells, with a chemotherapy control parameter, C(t), which we use to dynamically impose selection pressure on the sensitive subpopulation to slow tumor growth and manage competitive release of the resistant population. The adaptive chemoschedule is designed based on the deterministic (N→∞) adjusted replicator dynamical system, then implemented using the finite-cell stochastic frequency dependent Moran process model (N=10K-50K) to ascertain the cumulative effect of the stochastic fluctuations on the efficacy of the adaptive schedules over multiple rounds. We quantify the stochastic fixation probability regions of the R and S populations in the HSR trilinear phase plane as a function of the control parameter C∈[0,1], showing that the size of the R region increases with increasing C. We then implement an adaptive time-dependent schedule C(t) for the stochastic model and quantify the variances (using principal component coordinates) associated with the evolutionary cycles over multiple rounds of adaptive therapy. The variances increase subquadratically through several rounds before the evolutionary cycle begins to break down. Despite this, we show the stochastic adaptive schedules are more effective at delaying resistance than standard maximum tolerated dose and low-dose metronomic schedules. The simplified low-dimensional model provides some insights on how well multiple rounds of adaptive therapies are likely to perform over a range of tumor sizes (i.e., different values of N) if the goal is to maintain a sustained balance among competing subpopulations of cells to avoid chemoresistance via competitive release in a stochastic environment.

Ancestry-agnostic estimation of DNA sample contamination from sequence reads.

Detecting and estimating DNA sample contamination are important steps to ensure high-quality genotype calls and reliable downstream analysis. Existing methods rely on population allele frequency information for accurate estimation of contamination rates. Correctly specifying population allele frequencies for each individual in early stage of sequence analysis is impractical or even impossible for large-scale sequencing centers that simultaneously process samples from multiple studies across diverse populations. On the other hand, incorrectly specified allele frequencies may result in substantial bias in estimated contamination rates. For example, we observed that existing methods often fail to identify 10% contaminated samples at a typical 3% contamination exclusion threshold when genetic ancestry is misspecified. Such an incomplete screening of contaminated samples substantially inflates the estimated rate of genotyping errors even in deeply sequenced genomes and exomes. We propose a robust statistical method that accurately estimates DNA contamination and is agnostic to genetic ancestry of the intended or contaminating sample. Our method integrates the estimation of genetic ancestry and DNA contamination in a unified likelihood framework by leveraging individual-specific allele frequencies projected from reference genotypes onto principal component coordinates. Our method can also be used for estimating genetic ancestries, similar to LASER or TRACE, but simultaneously accounting for potential contamination. We demonstrate that our method robustly estimates contamination rates and genetic ancestries across populations and contamination scenarios. We further demonstrate that, in the presence of contamination, genetic ancestry inference can be substantially biased with existing methods that ignore contamination, while our method corrects for such biases.

INTERSTRAIN DIFFERENCES IN EMOTIONAL AND WEIGHT INDICES IN GC RATS WITH CATATONIC RESPONSE AND WISTAR RATS

In selecting rats for behavior, we observe a direct natural effect and affect the nonspecific stress function. In this process, new behavioral phenotypes appear in the strain under selection. They differ from the selected forms in the selection criterion. In the GC strain, a large proportion of the so-called nervous rats emerge. The criterion presumes the selection for the long cataleptic freezing character, whereas the nervous rats display elevated motor excitement: running, jumping, and vocalization. The main purpose of our study was to assess phenotypic indices in GC rats (abbreviated from genetic and catatonia) and recognize principal components of variability for emotional and weight indices. Rats of the ancestral Wistar population were taken as control. The following indices were measured: time of cataleptic freezing, excitement level, blood pressure, acoustic startle response, seizure activity, and weights of the heart, kidneys, adrenals, and spleen. Multivariate analysis methods were applied: factor analysis and principal component analysis. We confirmed the inclination of GC rats of the generation studied to freezing in quiet surrounding and after a strong acoustic sti - mulus. More pronounced startle responses, moderate hypertension, and larger weights of the heart and adrenals were noted. Two principal variability components were recognized: startle amplitude (PC1) and morphofunctional variability (PC2). The figure shows different locations of Wistar and GC individuals in principal component coordinates. The principal component method confirmed the genetic relationship between the startle and nervousness responses. It was shown that in PC2 the indices of heart, kidney, adrenal, and spleen weight exert negative effects, whereas the effects of startle and nervousness were positive. In the same component, an increase in the startle and nervousness responses positively correlates with the relative weights of the heart and adrenals. Differences in the directions of the contributions to the second component of morphofunctional variability are discussed.

Identification of Lightning Stroke Due to Shielding Failure and Backflashover for Ultra-High-Speed Transmission-Line Protection

The principal function of a protection relay is to protect the electric power system (EPS) from intolerable phenomena, such as fault produced by lightning, which can produce deleterious effects on EPS. Both impedance estimation protection and transient-based protection have become the most used protection relays for the lightning stroke classification. However, it is clear that currently these techniques suffer from drawbacks, which cannot correctly classify lightning stroke signals with speed and precision balanced, and cannot correctly distinguish the lightning stroke with and without a fault. Therefore, in order to avoid maloperation, protection schemes must be able to classifier lightning strokes correctly. This paper presents a novel methodology to provide effective lightning transmission-line protection. Principal component analysis is used to extract different patterns for lightning strokes due to shielding failure and backflashover. This work discloses that the transient voltages projected on the principal component coordinates clearly illustrate lightning strokes with and without a fault. In this manner, two pattern classifiers, based on spectral energy and artificial neural network, are useful for effectively classifying lightning stroke types. On the other hand, to evaluate the robustness of the proposed methodology, this research is also compared with the technique based on the wavelet transform, which has been the major tool used for the analysis of lightning strokes. Results show that the methodology proposed is fast and effective, which is not affected by operational conditions. Thus, this methodology can be applicable for high-speed protection systems.

Textile colour matching using linear and exponential weighted principal component analysis

In this work, linear and exponential weighted principal component analysis techniques based on spectral similarity were employed for the prediction of dye concentration in coloured fabrics, which had been dyed with three component dye mixtures. The matching strategy was based on the equalisation of the first three principal component coordinates of the weighted reflectance curves of the predicted and target sample in a dynamic 3D eigenvector space. The performance of the proposed algorithm was evaluated by the root mean square differences of the reflectance curves and the relative error of the concentration prediction, as well as the metamerism index. The obtained results indicated that the developed exponential weighted principal component analysis method is more accurate than the spectrophotometric method and the simple principal component analysis matching strategy.

High-speed transmission line protection using principal component analysis, a deterministic algorithm

A novel methodology to provide effective fault protection featuring high speed and excellent selectivity is presented. Principal component analysis made up of a set of travelling wave current signals, measured at one end of the transmission line, exhibits a well-defined pattern for faults of the same type. This work discloses that the fault current signal projected on the principal component coordinates can define the position, the insertion angle and the fault type (single-phase to ground, isolated two-phase, two-phase to ground and three-phase faults) on transmission lines. In this manner, it is possible to effectively categorise internal faults as well as distinguish when the fault is outside the line. Time windows less than a quarter of cycle of the power frequency are used to measure the current signals, thus the methodology is applicable for high-speed protection systems.

Differentiation of wild boar and domestic pig populations based on the frequency of chromosomes carrying endogenous retroviruses

Analysis of the frequencies of chromosomes carrying various classes of porcine endogenous retroviruses (PERVs) and combinations of these classes was performed in the swine species Sus scrofa L. by using maps constructed in two principal component coordinates. Four population clusters can be recognized in the maps. Cluster 1 is formed by wild boars,cluster 2 by domestic meat breeds, cluster 3 by meat-and-lard (universal) breeds, and cluster 4 by miniature pigs. The maps indicate that modern domesticated swine meat breeds are the closest to the wild type. Meat-and-lard domestic swine breeds are more distant from wild boars, and miniature pigs are diverged the most. The maps showed that microevolution processes associated with PERV carriership frequency had two basic dimensions, or vectors: the vector of fat deposition variation and the “minus” selection vector (determination of commercial traits). Thus, PERVs may cause variation in pig physiology.

A New matching strategy: Trial of the principal component coordinates

AbstractA new matching strategy based on the equalization of the first three principal component coordinates of sample and target in a 3D eigenvector space is stated. Two series of databases including 1269 specimens of Munsell Color Book and a virtual sample population of textile materials were selected. Their first three basis functions were extracted and considered as axes of eigenvector space. The principal component coordinates of two different collections of textile samples were determined in these spaces and considered as matching criteria. The performance of the proposed algorithm is evaluated by the color difference values under different light sources as well as the root mean square differences of reflectance curves. Results indicate some types of improvements in comparison with previous algorithms in terms of spectral as well as colorimetric accuracy. © 2007 Wiley Periodicals, Inc. Col Res Appl, 33, 10–18, 2008

Basis Functions of the Total Radiance Factor of Fluorescent Whitening Agents

This paper describes the representation of the total radiance factor of fluorescent whitening agents by up to three basis components. Applying the dimensionality reduction technique to the total radiance factor of 84 cotton samples treated with different fluorescent whitening agents, it was possible to reconstruct the spectral behavior of specimens by using a very small number of basis functions, accurately. In order to study the properties of the basis function, a three-dimensional Euclidean spectral space was implemented to represent the samples. The orientation of the samples that confirmed the whiteness index limitations of CIE1982 were along a line in this space, as expected from a set of white specimens. The perfect correlation was also found between the CIE1982 whiteness index, W, and the first derived principal component coordinates of the samples.